The broad, long-term objective of this research is to develop real-time dynamic cardiovascular risk indices to help predict outcomes and guide management of high-risk patients undergoing complex procedures. As high-cardiovascular risk patients present for complex cardiovascular surgery, the frequency of major adverse perioperative events (MAE) such as stroke, heart failure, and myocardial infarction has increased and is associated with longer hospitalization, increased mortality, and increased health care costs. Traditional static metrics of risk stratification, such as age or co morbid conditions, are not able to predict which patients are at risk for MAE or offer insights into individualized treatment strategies. These approaches fail to take into account fluctuations in physiologic control in individual patents and use simplified linear models that are unable to capture the complex, time varying features that are hallmarks of 'real-world' physiological signals. This project will apply state-of-the-art nonlinear methods to real-time intraoperative blood pressure signals and create a novel dynamical set of indices that facilitate early detection of subtle intraoperative hemodynamic disturbances. By relating these complex intraoperative signals to MAE, determined from the validated Society of Thoracic Surgeons (STS) National outcomes database, hemodynamic signatures with predictive value for MAE will be determined. To achieve these goals, the three specific aims of the proposed program are: 1) To determine a) if BPV is fixed for a given individual at various stages of surgery, b) BPV's predictive ability for postoperative MAE following cardiac surgery 2) To test the change in BPV from baseline to post-CPB periods as more predictive of MAE than either baseline or post-CPB BPV and to validate BPV's predictive ability of outcome 3) To create a unique open access database (preoperative, intraoperative hemodynamic signal recordings plus postoperative outcome data) publicly available via the NIH-sponsored PhysioNet Research Resource for Complex Physiologic Signals (www.physionet.org). The intraoperative beat-by-beat hemodynamic data will be collected directly from the operating room monitors and will be integrated with the automated anesthesia information systems and STS outcome database. The data will be deidentified and analyzed with multi scale entropy. The entropy data will be tested for its MAE predictive ability and compared to the traditional STS risk indices by itself or as a part of it. The entropy range at which the postoperative outcome is optimal will be determined and used as guidance for future interventional studies. The proposed dynamic approach offers a promising solution for patient level discrimination; improve patient counseling, intraoperative hemodynamic management and postoperative outcome.